Overview

Dataset statistics

Number of variables16
Number of observations3351
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory396.2 KiB
Average record size in memory121.1 B

Variable types

DateTime1
Categorical3
Numeric12

Warnings

username has constant value "JohnLegere" Constant
tweet has a high cardinality: 3351 distinct values High cardinality
mentions is highly correlated with hashtags and 1 other fieldsHigh correlation
hashtags is highly correlated with mentions and 2 other fieldsHigh correlation
video is highly correlated with hashtags and 2 other fieldsHigh correlation
photos is highly correlated with video and 1 other fieldsHigh correlation
number of tweets is highly correlated with mentions and 3 other fieldsHigh correlation
mentions is highly correlated with hashtags and 7 other fieldsHigh correlation
hashtags is highly correlated with mentions and 7 other fieldsHigh correlation
video is highly correlated with mentions and 6 other fieldsHigh correlation
photos is highly correlated with mentions and 5 other fieldsHigh correlation
urls is highly correlated with retweets_countHigh correlation
replies_count is highly correlated with mentions and 5 other fieldsHigh correlation
retweets_count is highly correlated with mentions and 8 other fieldsHigh correlation
likes_count is highly correlated with mentions and 6 other fieldsHigh correlation
number of tweets is highly correlated with mentions and 7 other fieldsHigh correlation
price is highly correlated with mentions and 3 other fieldsHigh correlation
mentions is highly correlated with hashtags and 2 other fieldsHigh correlation
hashtags is highly correlated with mentions and 2 other fieldsHigh correlation
video is highly correlated with photos and 1 other fieldsHigh correlation
photos is highly correlated with video and 1 other fieldsHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with mentions and 3 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
number of tweets is highly correlated with mentions and 3 other fieldsHigh correlation
urls is highly correlated with mentionsHigh correlation
replies_count is highly correlated with retweets_countHigh correlation
video is highly correlated with photos and 2 other fieldsHigh correlation
mentions is highly correlated with urls and 1 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
photos is highly correlated with video and 2 other fieldsHigh correlation
bins is highly correlated with percent changeHigh correlation
number of tweets is highly correlated with video and 2 other fieldsHigh correlation
price is highly correlated with hashtagsHigh correlation
hashtags is highly correlated with video and 4 other fieldsHigh correlation
likes_count is highly correlated with retweets_countHigh correlation
percent change is highly correlated with binsHigh correlation
bins is highly correlated with usernameHigh correlation
username is highly correlated with binsHigh correlation
retweets_count is highly skewed (γ1 = 24.98091088) Skewed
tweet is uniformly distributed Uniform
date has unique values Unique
tweet has unique values Unique
mentions has 1017 (30.3%) zeros Zeros
hashtags has 771 (23.0%) zeros Zeros
cashtags has 3275 (97.7%) zeros Zeros
video has 670 (20.0%) zeros Zeros
photos has 787 (23.5%) zeros Zeros
urls has 613 (18.3%) zeros Zeros
percent change has 38 (1.1%) zeros Zeros

Reproduction

Analysis started2021-09-27 19:04:49.067099
Analysis finished2021-09-27 19:05:10.249818
Duration21.18 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

date
Date

UNIQUE

Distinct3351
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size26.3 KiB
Minimum2016-08-23 09:30:00
Maximum2021-07-20 16:00:00
2021-09-27T15:05:10.367982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:10.527157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tweet
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct3351
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size26.3 KiB
@Azphxmex @art_lucero - please look into it ASAP @mgcarley @TMobile You’re wearing the wrong color ;) @tsukinoxo @TMobile do it!!! @mikusz @ATT @TMobile @ATTCares Good choice :)))) @Abruuh_ @TMobile Welcome back! Ready to #GetThanked?! @Mikesmith2317 Come on over!! @Prttyawsmcool Email me! John.legere@t-mobile.com Well, there goes my productivity for the day https://t.co/vxZTn2raCG The future of watching the #Olympics is on mobile, but I think we already knew that! ;) https://t.co/FCsSqRPJMN @isnotgraham @art_lucero john.legere@t-mobile.com @isnotgraham @art_lucero can you email me? Oooo, I need this! @thenorthface, how do I get one in #magenta?!? #gadgetlover https://t.co/xWKZXcSd5X More things to #BingeOn from @Netflix!! https://t.co/Y8IikGMJH9 Hey @MichaelPhelps - I've got your back!! :)))) Watch this: https://t.co/B1XR0MHSjk https://t.co/vnsLUmLuCg Interesting arguments for/against wearables on the field. #MLB https://t.co/IRraM3onkr Use these tips to get tickets, and @tmobile to get free wifi while you’re flying. #tmobilewingman https://t.co/jwffPWCP9F
 
1
@ThePudgeyFellow @TMobile Never too early to build good habits😆 Happy birthday @DavidCarey! Tuesdays would not be the same without you! https://t.co/999SyPCt9c @Chris__Michaels Enjoy! @EricTKinman @TMobile enjoy!! @Colleen_Clare @Megan_Rosebud @TMobile enjoy!
 
1
@halohuh https://t.co/UjuXdZnwmv @alittleharrisy @TMobile @UW @tmobilecareers 🙌 @mstrongjr 🤔 @women_yang https://t.co/OjSGTgiWJb There’s really no bad pizza but this is the perfect combo in my opinion! What’s your go-to pizza topping? 🍕 https://t.co/mjZvg6ojoL GREAT 🙄 Who let @verHIDEzon buy ad space outside our NY Signature Store⁉️ #5GDoneWrong https://t.co/7XkdfM9hcS @s_brownie @AuntieAnnes @TMobile https://t.co/hFyxRDtvVf THIS IS ABSURD! These guys even have a twitter account?! 🤦‍♂️🤦‍♂️🤦‍♂️ @VerHIDEzon https://t.co/Hn0AiQugRT @majeikstagram @BKaminsky https://t.co/dd9mrLQTpB @Samuelg33791554 you're forgiven @millionmiler24 https://t.co/yg5uF7tyoa @laughlovego @TMobile @AuntieAnnes enjoy! Have you seen this new company verHIDEzon? Looks like they are forcing their customers to pay more for 5G… but not telling them where they can find it? Sounds familiar… #5GDoneWrong https://t.co/X6MhEOH1GI @Samuelg33791554 https://t.co/4m6fHsr9Ok
 
1
Something had to be done. @Verizon’s been talking a big game about 5G, charging more for it and not even giving customers a map to find it. Hopefully all of NYC (and the rest of the country) now realizes @verHIDEzon = Verizon 👀 @MusicalMoore6 @TMobile @TMobileHelp https://t.co/qxaepJiKKR @jvkap @TMobile Go team! 🎉 In 51 hours, the new #iPhone11, #iPhone11Pro and #iPhone11ProMax can be seen by your very own eyes 👀 🛸 #StormTMobile https://t.co/26TaNwcDLb @MarcSaint9 K. @Cyandashie https://t.co/GkwDbBC964
 
1
@joecro1984 https://t.co/U5qs9qst4p @xurvisgaming https://t.co/4n2W62S6Pp @in4birdie 🙌🏼🙌🏼🙌🏼 @Big_Problems_ https://t.co/e93zg4BQcL @riskybiz64 https://t.co/7dJVTjmHUz I was told I shouldn’t post this... https://t.co/9psoqgF7DM @yeetus_meetus @KoboldCrusader https://t.co/rk7ycuRkX6 @Jack_bibi tell everyone you meet to join @TMobile!!!
 
1
Other values (3346)
3346 

Length

Max length7236
Median length666
Mean length830.9755297
Min length23

Characters and Unicode

Total characters2784599
Distinct characters798
Distinct categories21 ?
Distinct scripts6 ?
Distinct blocks21 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3351 ?
Unique (%)100.0%

Sample

1st row@Azphxmex @art_lucero - please look into it ASAP @mgcarley @TMobile You’re wearing the wrong color ;) @tsukinoxo @TMobile do it!!! @mikusz @ATT @TMobile @ATTCares Good choice :)))) @Abruuh_ @TMobile Welcome back! Ready to #GetThanked?! @Mikesmith2317 Come on over!! @Prttyawsmcool Email me! John.legere@t-mobile.com Well, there goes my productivity for the day https://t.co/vxZTn2raCG The future of watching the #Olympics is on mobile, but I think we already knew that! ;) https://t.co/FCsSqRPJMN @isnotgraham @art_lucero john.legere@t-mobile.com @isnotgraham @art_lucero can you email me? Oooo, I need this! @thenorthface, how do I get one in #magenta?!? #gadgetlover https://t.co/xWKZXcSd5X More things to #BingeOn from @Netflix!! https://t.co/Y8IikGMJH9 Hey @MichaelPhelps - I've got your back!! :)))) Watch this: https://t.co/B1XR0MHSjk https://t.co/vnsLUmLuCg Interesting arguments for/against wearables on the field. #MLB https://t.co/IRraM3onkr Use these tips to get tickets, and @tmobile to get free wifi while you’re flying. #tmobilewingman https://t.co/jwffPWCP9F
2nd row@silods yes! Very proud of @UMassAmherst and to have studied there! https://t.co/sjK4IbTy7z @iSocialFanz possibly :)) This is hilarious, but also brilliant!! https://t.co/9wtLj2J0qa I might wear one if it were #magenta, @Xbox…. ;) https://t.co/Mk3EI3Q37R #TuesdayMotivation: Today is #TMobileTuesdays & you can #GetThanked w/ awesome prizes from @Orbitz!! #TMobileTuesdays is giving away a chance to win tickets to anywhere in North America!! Where would u go?!? https://t.co/8FGkPmbUYG
3rd row@Woofdah @TMobile What are you going to rent?! @FreelancerJenny 👍🏼 @CURLZ_92 The best day of the week!! @SCAFFBEEZYjr They should switch! @lennahs @TMobile good choice! I think this means I technically have a gold medal ;) https://t.co/CC9KXb07Ch Add this to the list of ways not to impress a date https://t.co/gIGqMvBd1t So great!! You need to hear what we’re cooking up for @TMobileAtWork customers, I think you’d like it! https://t.co/lTCXDP5ero Watching #TheProfit, love the show. @MarcusLemonis does this shirt come in magenta?! https://t.co/S262GTf1OQ @irusanovschi stop thinking and come over!!!! @_Lucky45 i read all email and follow up... No boiler rooms @TMobile :) It’s #TravelTuesday & #TMobileTuesdays!! Coincidence?? Nope!! Now, go try to win cool @Orbitz stuff!! I wonder what would come up if you draw @ATT?! #DeatthStar LOL https://t.co/MvuHgF7QxD Today I dress in what I love! All @TMobile (well I do every day)! ...it's also a great way to meet people :)) https://t.co/iqQAaTLXnm Flying out of Seattle on a beautiful day... Many stops ahead in next week..a lot of @TMobile people to thank! https://t.co/IQ5n8Yz6L6
4th rowSunscreen doesn’t smell great, but I don’t think fried chicken-scented sunscreen is any better… https://t.co/VT6qgGquMK Our thoughts & prayers are with 🇮🇹. Fees for @TMobile custs to call/text to/from Italy will be waived until 8/28. https://t.co/wcw2ZY98aL I read every email I get, no matter what :))))) https://t.co/AURnzcFZV5 Is this what the future looks like??? https://t.co/3KU7cnqv5f
5th row@MillerPhelan Thank you!! @LIVEWlRED You should! @Beverly330 @TMobile welcome!! @petersterne I ❤️ @TMobile ;) @ArimHerrera Switch to @TMobile! :)))) @ItsTonyWhite There’s always next week! @AlienReconstar Set reminders! ;) Great day for a run in @CentralParkNYC of course in all @TMobile #Magenta https://t.co/pK5yZZQZmB

Common Values

ValueCountFrequency (%)
@Azphxmex @art_lucero - please look into it ASAP @mgcarley @TMobile You’re wearing the wrong color ;) @tsukinoxo @TMobile do it!!! @mikusz @ATT @TMobile @ATTCares Good choice :)))) @Abruuh_ @TMobile Welcome back! Ready to #GetThanked?! @Mikesmith2317 Come on over!! @Prttyawsmcool Email me! John.legere@t-mobile.com Well, there goes my productivity for the day https://t.co/vxZTn2raCG The future of watching the #Olympics is on mobile, but I think we already knew that! ;) https://t.co/FCsSqRPJMN @isnotgraham @art_lucero john.legere@t-mobile.com @isnotgraham @art_lucero can you email me? Oooo, I need this! @thenorthface, how do I get one in #magenta?!? #gadgetlover https://t.co/xWKZXcSd5X More things to #BingeOn from @Netflix!! https://t.co/Y8IikGMJH9 Hey @MichaelPhelps - I've got your back!! :)))) Watch this: https://t.co/B1XR0MHSjk https://t.co/vnsLUmLuCg Interesting arguments for/against wearables on the field. #MLB https://t.co/IRraM3onkr Use these tips to get tickets, and @tmobile to get free wifi while you’re flying. #tmobilewingman https://t.co/jwffPWCP9F1
 
< 0.1%
@ThePudgeyFellow @TMobile Never too early to build good habits😆 Happy birthday @DavidCarey! Tuesdays would not be the same without you! https://t.co/999SyPCt9c @Chris__Michaels Enjoy! @EricTKinman @TMobile enjoy!! @Colleen_Clare @Megan_Rosebud @TMobile enjoy!1
 
< 0.1%
@halohuh https://t.co/UjuXdZnwmv @alittleharrisy @TMobile @UW @tmobilecareers 🙌 @mstrongjr 🤔 @women_yang https://t.co/OjSGTgiWJb There’s really no bad pizza but this is the perfect combo in my opinion! What’s your go-to pizza topping? 🍕 https://t.co/mjZvg6ojoL GREAT 🙄 Who let @verHIDEzon buy ad space outside our NY Signature Store⁉️ #5GDoneWrong https://t.co/7XkdfM9hcS @s_brownie @AuntieAnnes @TMobile https://t.co/hFyxRDtvVf THIS IS ABSURD! These guys even have a twitter account?! 🤦‍♂️🤦‍♂️🤦‍♂️ @VerHIDEzon https://t.co/Hn0AiQugRT @majeikstagram @BKaminsky https://t.co/dd9mrLQTpB @Samuelg33791554 you're forgiven @millionmiler24 https://t.co/yg5uF7tyoa @laughlovego @TMobile @AuntieAnnes enjoy! Have you seen this new company verHIDEzon? Looks like they are forcing their customers to pay more for 5G… but not telling them where they can find it? Sounds familiar… #5GDoneWrong https://t.co/X6MhEOH1GI @Samuelg33791554 https://t.co/4m6fHsr9Ok1
 
< 0.1%
Something had to be done. @Verizon’s been talking a big game about 5G, charging more for it and not even giving customers a map to find it. Hopefully all of NYC (and the rest of the country) now realizes @verHIDEzon = Verizon 👀 @MusicalMoore6 @TMobile @TMobileHelp https://t.co/qxaepJiKKR @jvkap @TMobile Go team! 🎉 In 51 hours, the new #iPhone11, #iPhone11Pro and #iPhone11ProMax can be seen by your very own eyes 👀 🛸 #StormTMobile https://t.co/26TaNwcDLb @MarcSaint9 K. @Cyandashie https://t.co/GkwDbBC9641
 
< 0.1%
@joecro1984 https://t.co/U5qs9qst4p @xurvisgaming https://t.co/4n2W62S6Pp @in4birdie 🙌🏼🙌🏼🙌🏼 @Big_Problems_ https://t.co/e93zg4BQcL @riskybiz64 https://t.co/7dJVTjmHUz I was told I shouldn’t post this... https://t.co/9psoqgF7DM @yeetus_meetus @KoboldCrusader https://t.co/rk7ycuRkX6 @Jack_bibi tell everyone you meet to join @TMobile!!!1
 
< 0.1%
@LmtlessLi @TMobileArena Congrats! You're getting two 🎟 to @iHeartFestival! @TMobileHelp will reach out to get your details. @adrianrosales Congrats! You're getting two 🎟 to @iHeartFestival! @TMobileHelp will reach out to get your details. @joshrobertnay @BurgerKing you can do that too! @Torre_Green I just want to be friends Torre @YOUNGSHINE2 @BurgerKing but they're real @Resqme29445 Do it! Like I always say... Shut up and listen to your customers!! You told us you wanted the @BurgerKing 👑 Impossible Whopper and now it's yours! Get your FREE Impossible Whopper 🍔 on next week's #TMobileTuesdays https://t.co/J1UCdLuO5K @hypno_squid this is a good one!😂😂 @JoeCool205 @TMobileHelp @SokieSok https://t.co/7N0F4MuAXx1
 
< 0.1%
@BlackNGoldBunny @BurgerKing https://t.co/w8OyBUtmYS @BlackNGoldBunny @BurgerKing https://t.co/H8mXdsGD1K Happy birthday @JimmyFallon! I made you a #SlowCookerSunday cake! 😂🎂 #smile https://t.co/CBtweVUefD @GucciRatz https://t.co/NTCAnS9a1r @Actual_Andre @VerHIDEzon 😉 @jaredmroberts @StaneffMatt 🤔🤔🤔 There are so many incredible women that make @TMobile the amazing place it is to work and I couldn’t be more proud to have made @Fortunes list!1
 
< 0.1%
@JeanneMallett @TMobile great photo! @MignonClyburn I’m sorry for your immeasurable loss. My prayers go out to you, @WhipClyburn and your family. She was an amazing woman who leaves us a remarkable legacy. As you can 👀 the real party is at @TMobile!! 🎉 The new iPhones have landed 🛸📱Use #StormTMobile to share why this phone is OUT OF THIS WORLD and I’ll pick my favorite to win one👽 @ErickG3m1n1 @TMobile woo hoo!!! @napier03 @TMobile @BurgerKing https://t.co/Q872cT129j So who’s going? Bring me back a UFO! #Area51 https://t.co/QqlqxanpSF They're hereeeeee 🛸📱#StormTMobile NOW to get your new #iPhone11, #iPhone11Pro or #iPhone11ProMax!! https://t.co/Jwt9L2uLBR @iamalyxander @TMobile @TMobileHelp Email me john.legere@t-mobile.com @IamVeRoNiCaG @TMobile nice! enjoy! @CoreyTa32653536 https://t.co/VF25U87aXq @JeffChausse Do not believe this. @StevenCastroJr1 @AguirreSaralily @TMobile @Shell 🙌🏼🙌🏼🙌🏼1
 
< 0.1%
@mrbrauer I couldn't agree more 🤩 Thank you A.B. for sharing your story with the world – your courage and honesty is an inspiration! #WeAreTMobile I admire the effort here, but do kids these days know what string cheese is? They do come in all flavors….check your local grocery store…😂 https://t.co/e9n0gDhAQj @Isabell22107618 @TMobile @ATT @verizon 😁😬😁 @Bandk4me @TMobile woo hoo! welcome to @TMobile https://t.co/rrXeMKIenG Happy #RuffFriday!! Meet Lala, an adorable 2-year-old pup who loves a good ol’ game of fetch and being with her people. She’s looking for a home where her future family will be around a lot to cuddle and play with her. Head to @SeattleHumane to meet her today! @aidenjamestour enjoy! @vgkhozier for you Jess, I do. 👀1
 
< 0.1%
@Arg1031NY @TMobile You win! But you forgot to mention our newest, most powerful 600 MHz spectrum 😉 @TMobileHelp will reach out to get your info. @JaimeTPA @TMobile You win! iPhone 11 + T-Mobile Network + Winning one for FREE = win/win/win!! @TMobileHelp will reach out to get your info. CONGRATS to our #runscope #BatmanDay winners @Brandonsarsori1, @Truthster11 and @SOSpleaz for winning an Apple Watch Series 5 and @AbuSamak for winning an iPhone 11 (or Apple Watch Series 5). Good job on knowing so much about me (Batman)!!! #LIVE @TMobile CEO: #Runscope and #applewatch #giveaway time from @CentralParkNYC on #BatmanDay! Join me NOW! https://t.co/jyUS4Hobr2 I am on a run @CentralParkNYC ... maybe it’s time for a #Runscope #BatmanDay #Trivia #Giveaway ? https://t.co/oxYJr012qe It’s the last weekend of summer, CAN YOU BELIEVE IT!? How are you taking advantage of the last weekend in summer? Ridin’ around in my batmobile! Happy ME day everyone!! #BatmanDay #IAmBatman https://t.co/njdJrfdpFu1
 
< 0.1%
Other values (3341)3341
99.7%

Length

2021-09-27T15:05:10.884204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tmobile11032
 
3.0%
to9884
 
2.7%
the8284
 
2.2%
you6812
 
1.8%
a4975
 
1.3%
and4663
 
1.3%
i4651
 
1.3%
4436
 
1.2%
for3828
 
1.0%
it3411
 
0.9%
Other values (58664)307002
83.2%

Most occurring characters

ValueCountFrequency (%)
385250
 
13.8%
e206196
 
7.4%
t174423
 
6.3%
o171214
 
6.1%
a128729
 
4.6%
i116363
 
4.2%
s102832
 
3.7%
n102647
 
3.7%
r95040
 
3.4%
l88340
 
3.2%
Other values (788)1213565
43.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1779381
63.9%
Space Separator385553
 
13.8%
Uppercase Letter268304
 
9.6%
Other Punctuation239406
 
8.6%
Decimal Number67151
 
2.4%
Other Symbol15766
 
0.6%
Final Punctuation7759
 
0.3%
Connector Punctuation5822
 
0.2%
Dash Punctuation4471
 
0.2%
Close Punctuation3671
 
0.1%
Other values (11)7315
 
0.3%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
🙌2076
 
13.2%
😂1358
 
8.6%
😉1019
 
6.5%
👍591
 
3.7%
😎377
 
2.4%
🎉353
 
2.2%
🤔338
 
2.1%
325
 
2.1%
😊306
 
1.9%
😁281
 
1.8%
Other values (643)8742
55.4%
Uppercase Letter
ValueCountFrequency (%)
T33388
 
12.4%
M26639
 
9.9%
S15918
 
5.9%
I15254
 
5.7%
A12701
 
4.7%
C11678
 
4.4%
W11296
 
4.2%
E10318
 
3.8%
L10027
 
3.7%
D9717
 
3.6%
Other values (33)111368
41.5%
Lowercase Letter
ValueCountFrequency (%)
e206196
11.6%
t174423
 
9.8%
o171214
 
9.6%
a128729
 
7.2%
i116363
 
6.5%
s102832
 
5.8%
n102647
 
5.8%
r95040
 
5.3%
l88340
 
5.0%
h79793
 
4.5%
Other values (20)513804
28.9%
Other Punctuation
ValueCountFrequency (%)
@58062
24.3%
/52367
21.9%
!38549
16.1%
.33654
14.1%
:20005
 
8.4%
#12237
 
5.1%
?7142
 
3.0%
,6439
 
2.7%
;3523
 
1.5%
'2922
 
1.2%
Other values (9)4506
 
1.9%
Decimal Number
ValueCountFrequency (%)
110700
15.9%
28392
12.5%
07987
11.9%
36166
9.2%
55934
8.8%
45871
8.7%
75767
8.6%
85586
8.3%
95563
8.3%
65185
7.7%
Modifier Symbol
ValueCountFrequency (%)
🏼2271
67.8%
🏻938
28.0%
¯60
 
1.8%
🏽51
 
1.5%
🏿17
 
0.5%
^7
 
0.2%
5
 
0.1%
´3
 
0.1%
Math Symbol
ValueCountFrequency (%)
+259
48.8%
=224
42.2%
16
 
3.0%
~14
 
2.6%
|14
 
2.6%
2
 
0.4%
1
 
0.2%
1
 
0.2%
Space Separator
ValueCountFrequency (%)
385250
99.9%
 296
 
0.1%
6
 
< 0.1%
 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-4010
89.7%
456
 
10.2%
5
 
0.1%
Final Punctuation
ValueCountFrequency (%)
7395
95.3%
363
 
4.7%
»1
 
< 0.1%
Format
ValueCountFrequency (%)
375
99.5%
1
 
0.3%
1
 
0.3%
Connector Punctuation
ValueCountFrequency (%)
_5818
99.9%
_4
 
0.1%
Close Punctuation
ValueCountFrequency (%)
)3670
> 99.9%
]1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
(553
99.8%
[1
 
0.2%
Initial Punctuation
ValueCountFrequency (%)
363
88.1%
49
 
11.9%
Nonspacing Mark
ValueCountFrequency (%)
1255
100.0%
Currency Symbol
ValueCountFrequency (%)
$752
100.0%
Other Number
ValueCountFrequency (%)
½1
100.0%
Enclosing Mark
ValueCountFrequency (%)
50
100.0%
Other Letter
ValueCountFrequency (%)
30
100.0%
Private Use
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2047662
73.5%
Common735207
 
26.4%
Inherited1680
 
0.1%
Katakana30
 
< 0.1%
Braille19
 
< 0.1%
Unknown1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
385250
52.4%
@58062
 
7.9%
/52367
 
7.1%
!38549
 
5.2%
.33654
 
4.6%
:20005
 
2.7%
#12237
 
1.7%
110700
 
1.5%
28392
 
1.1%
07987
 
1.1%
Other values (726)108004
 
14.7%
Latin
ValueCountFrequency (%)
e206196
 
10.1%
t174423
 
8.5%
o171214
 
8.4%
a128729
 
6.3%
i116363
 
5.7%
s102832
 
5.0%
n102647
 
5.0%
r95040
 
4.6%
l88340
 
4.3%
h79793
 
3.9%
Other values (46)782085
38.2%
Inherited
ValueCountFrequency (%)
1255
74.7%
375
 
22.3%
50
 
3.0%
Katakana
ValueCountFrequency (%)
30
100.0%
Unknown
ValueCountFrequency (%)
1
100.0%
Braille
ValueCountFrequency (%)
19
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2753063
98.9%
Punctuation10736
 
0.4%
None10449
 
0.4%
Emoticons6871
 
0.2%
VS1255
 
< 0.1%
Dingbats842
 
< 0.1%
Misc Symbols727
 
< 0.1%
Latin 1 Sup368
 
< 0.1%
Enclosed Alphanum Sup148
 
< 0.1%
Misc Technical38
 
< 0.1%
Other values (11)102
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
385250
 
14.0%
e206196
 
7.5%
t174423
 
6.3%
o171214
 
6.2%
a128729
 
4.7%
i116363
 
4.2%
s102832
 
3.7%
n102647
 
3.7%
r95040
 
3.5%
l88340
 
3.2%
Other values (80)1182029
42.9%
Punctuation
ValueCountFrequency (%)
7395
68.9%
1648
 
15.4%
456
 
4.2%
375
 
3.5%
363
 
3.4%
363
 
3.4%
49
 
0.5%
44
 
0.4%
16
 
0.1%
14
 
0.1%
Other values (4)13
 
0.1%
None
ValueCountFrequency (%)
🏼2271
21.7%
🏻938
 
9.0%
👍591
 
5.7%
🎉353
 
3.4%
🤔338
 
3.2%
📱250
 
2.4%
👀195
 
1.9%
🏃179
 
1.7%
💖156
 
1.5%
🐶147
 
1.4%
Other values (503)5031
48.1%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇺41
27.7%
🇸40
27.0%
🆓29
19.6%
🇵8
 
5.4%
🇮5
 
3.4%
🇹5
 
3.4%
🇷5
 
3.4%
🆕4
 
2.7%
🇭3
 
2.0%
🇩2
 
1.4%
Other values (5)6
 
4.1%
Dingbats
ValueCountFrequency (%)
325
38.6%
209
24.8%
126
 
15.0%
54
 
6.4%
52
 
6.2%
23
 
2.7%
14
 
1.7%
6
 
0.7%
5
 
0.6%
5
 
0.6%
Other values (10)23
 
2.7%
VS
ValueCountFrequency (%)
1255
100.0%
Emoticons
ValueCountFrequency (%)
🙌2076
30.2%
😂1358
19.8%
😉1019
14.8%
😎377
 
5.5%
😊306
 
4.5%
😁281
 
4.1%
😍182
 
2.6%
😏153
 
2.2%
😱101
 
1.5%
😅92
 
1.3%
Other values (54)926
13.5%
Misc Symbols
ValueCountFrequency (%)
224
30.8%
113
15.5%
55
 
7.6%
49
 
6.7%
46
 
6.3%
41
 
5.6%
37
 
5.1%
19
 
2.6%
15
 
2.1%
13
 
1.8%
Other values (26)115
15.8%
Latin 1 Sup
ValueCountFrequency (%)
 296
80.4%
¯60
 
16.3%
é4
 
1.1%
´3
 
0.8%
½1
 
0.3%
ä1
 
0.3%
è1
 
0.3%
»1
 
0.3%
ñ1
 
0.3%
Playing Cards
ValueCountFrequency (%)
🃏1
100.0%
Misc Technical
ValueCountFrequency (%)
22
57.9%
7
 
18.4%
4
 
10.5%
3
 
7.9%
1
 
2.6%
1
 
2.6%
Katakana
ValueCountFrequency (%)
30
100.0%
Specials
ValueCountFrequency (%)
4
100.0%
PUA
ValueCountFrequency (%)
1
100.0%
Arrows
ValueCountFrequency (%)
1
50.0%
1
50.0%
Braille
ValueCountFrequency (%)
19
100.0%
Geometric Shapes
ValueCountFrequency (%)
16
100.0%
Math Alphanum
ValueCountFrequency (%)
𝑬4
17.4%
𝑶2
 
8.7%
𝑰2
 
8.7%
𝑳2
 
8.7%
𝑻1
 
4.3%
𝑴1
 
4.3%
𝑩1
 
4.3%
𝑿1
 
4.3%
𝑪1
 
4.3%
𝑼1
 
4.3%
Other values (7)7
30.4%
Sup Arrows B
ValueCountFrequency (%)
2
100.0%
Geometric Shapes Ext
ValueCountFrequency (%)
🟠1
33.3%
🟣1
33.3%
🟢1
33.3%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

username
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.3 KiB
JohnLegere
3351 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters33510
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohnLegere
2nd rowJohnLegere
3rd rowJohnLegere
4th rowJohnLegere
5th rowJohnLegere

Common Values

ValueCountFrequency (%)
JohnLegere3351
100.0%

Length

2021-09-27T15:05:11.165684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:05:11.241067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
johnlegere3351
100.0%

Most occurring characters

ValueCountFrequency (%)
e10053
30.0%
J3351
 
10.0%
o3351
 
10.0%
h3351
 
10.0%
n3351
 
10.0%
L3351
 
10.0%
g3351
 
10.0%
r3351
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter26808
80.0%
Uppercase Letter6702
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10053
37.5%
o3351
 
12.5%
h3351
 
12.5%
n3351
 
12.5%
g3351
 
12.5%
r3351
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
J3351
50.0%
L3351
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin33510
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10053
30.0%
J3351
 
10.0%
o3351
 
10.0%
h3351
 
10.0%
n3351
 
10.0%
L3351
 
10.0%
g3351
 
10.0%
r3351
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII33510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e10053
30.0%
J3351
 
10.0%
o3351
 
10.0%
h3351
 
10.0%
n3351
 
10.0%
L3351
 
10.0%
g3351
 
10.0%
r3351
 
10.0%

mentions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.021486124
Minimum0
Maximum38
Zeros1017
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2021-09-27T15:05:11.318607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile11
Maximum38
Range38
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.900266084
Coefficient of variation (CV)1.290843619
Kurtosis7.917982874
Mean3.021486124
Median Absolute Deviation (MAD)2
Skewness2.340774776
Sum10125
Variance15.21207552
MonotonicityNot monotonic
2021-09-27T15:05:11.450556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
01017
30.3%
1536
16.0%
2448
13.4%
3330
 
9.8%
4237
 
7.1%
5187
 
5.6%
7121
 
3.6%
6116
 
3.5%
973
 
2.2%
865
 
1.9%
Other values (18)221
 
6.6%
ValueCountFrequency (%)
01017
30.3%
1536
16.0%
2448
13.4%
3330
 
9.8%
4237
 
7.1%
5187
 
5.6%
6116
 
3.5%
7121
 
3.6%
865
 
1.9%
973
 
2.2%
ValueCountFrequency (%)
381
 
< 0.1%
301
 
< 0.1%
271
 
< 0.1%
246
0.2%
237
0.2%
222
 
0.1%
214
0.1%
202
 
0.1%
195
0.1%
186
0.2%

hashtags
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct30
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.626977022
Minimum0
Maximum33
Zeros771
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2021-09-27T15:05:11.588435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile11
Maximum33
Range33
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.981888897
Coefficient of variation (CV)1.097853356
Kurtosis6.801975584
Mean3.626977022
Median Absolute Deviation (MAD)2
Skewness2.00937079
Sum12154
Variance15.85543919
MonotonicityNot monotonic
2021-09-27T15:05:11.718440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0771
23.0%
1477
14.2%
2422
12.6%
3356
10.6%
4284
 
8.5%
5245
 
7.3%
6191
 
5.7%
7136
 
4.1%
8111
 
3.3%
981
 
2.4%
Other values (20)277
 
8.3%
ValueCountFrequency (%)
0771
23.0%
1477
14.2%
2422
12.6%
3356
10.6%
4284
 
8.5%
5245
 
7.3%
6191
 
5.7%
7136
 
4.1%
8111
 
3.3%
981
 
2.4%
ValueCountFrequency (%)
332
0.1%
322
0.1%
311
 
< 0.1%
301
 
< 0.1%
291
 
< 0.1%
261
 
< 0.1%
241
 
< 0.1%
222
0.1%
214
0.1%
204
0.1%

cashtags
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06893464637
Minimum0
Maximum16
Zeros3275
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2021-09-27T15:05:11.841685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6684971107
Coefficient of variation (CV)9.697548996
Kurtosis267.3476388
Mean0.06893464637
Median Absolute Deviation (MAD)0
Skewness14.87541915
Sum231
Variance0.4468883871
MonotonicityNot monotonic
2021-09-27T15:05:11.955015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
03275
97.7%
138
 
1.1%
210
 
0.3%
39
 
0.3%
64
 
0.1%
44
 
0.1%
73
 
0.1%
82
 
0.1%
151
 
< 0.1%
101
 
< 0.1%
Other values (4)4
 
0.1%
ValueCountFrequency (%)
03275
97.7%
138
 
1.1%
210
 
0.3%
39
 
0.3%
44
 
0.1%
51
 
< 0.1%
64
 
0.1%
73
 
0.1%
82
 
0.1%
101
 
< 0.1%
ValueCountFrequency (%)
161
 
< 0.1%
151
 
< 0.1%
121
 
< 0.1%
111
 
< 0.1%
101
 
< 0.1%
82
0.1%
73
0.1%
64
0.1%
51
 
< 0.1%
44
0.1%

video
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.936436885
Minimum0
Maximum25
Zeros670
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2021-09-27T15:05:12.095023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile9
Maximum25
Range25
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.981007596
Coefficient of variation (CV)1.015178501
Kurtosis4.332311361
Mean2.936436885
Median Absolute Deviation (MAD)2
Skewness1.722116784
Sum9840
Variance8.886406285
MonotonicityNot monotonic
2021-09-27T15:05:12.256186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0670
20.0%
1636
19.0%
2537
16.0%
3452
13.5%
4324
9.7%
5204
 
6.1%
6139
 
4.1%
7108
 
3.2%
886
 
2.6%
956
 
1.7%
Other values (13)139
 
4.1%
ValueCountFrequency (%)
0670
20.0%
1636
19.0%
2537
16.0%
3452
13.5%
4324
9.7%
5204
 
6.1%
6139
 
4.1%
7108
 
3.2%
886
 
2.6%
956
 
1.7%
ValueCountFrequency (%)
251
 
< 0.1%
211
 
< 0.1%
202
 
0.1%
192
 
0.1%
182
 
0.1%
174
 
0.1%
161
 
< 0.1%
156
0.2%
148
0.2%
1313
0.4%

photos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.07191883
Minimum0
Maximum25
Zeros787
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2021-09-27T15:05:13.503829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile9
Maximum25
Range25
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.230806358
Coefficient of variation (CV)1.051722567
Kurtosis3.806946404
Mean3.07191883
Median Absolute Deviation (MAD)2
Skewness1.645286343
Sum10294
Variance10.43810972
MonotonicityNot monotonic
2021-09-27T15:05:13.632625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0787
23.5%
1566
16.9%
2433
12.9%
3411
12.3%
4323
9.6%
5210
 
6.3%
6168
 
5.0%
7130
 
3.9%
891
 
2.7%
969
 
2.1%
Other values (12)163
 
4.9%
ValueCountFrequency (%)
0787
23.5%
1566
16.9%
2433
12.9%
3411
12.3%
4323
9.6%
5210
 
6.3%
6168
 
5.0%
7130
 
3.9%
891
 
2.7%
969
 
2.1%
ValueCountFrequency (%)
251
 
< 0.1%
213
 
0.1%
204
 
0.1%
183
 
0.1%
175
 
0.1%
166
 
0.2%
156
 
0.2%
149
0.3%
1322
0.7%
1218
0.5%

urls
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.178752611
Minimum0
Maximum32
Zeros613
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2021-09-27T15:05:13.755002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum32
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.229913805
Coefficient of variation (CV)1.023481874
Kurtosis23.78758282
Mean2.178752611
Median Absolute Deviation (MAD)1
Skewness3.274763458
Sum7301
Variance4.972515578
MonotonicityNot monotonic
2021-09-27T15:05:13.877455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1913
27.2%
2726
21.7%
0613
18.3%
3468
14.0%
4272
 
8.1%
5146
 
4.4%
689
 
2.7%
747
 
1.4%
828
 
0.8%
915
 
0.4%
Other values (12)34
 
1.0%
ValueCountFrequency (%)
0613
18.3%
1913
27.2%
2726
21.7%
3468
14.0%
4272
 
8.1%
5146
 
4.4%
689
 
2.7%
747
 
1.4%
828
 
0.8%
915
 
0.4%
ValueCountFrequency (%)
321
 
< 0.1%
242
 
0.1%
231
 
< 0.1%
221
 
< 0.1%
191
 
< 0.1%
171
 
< 0.1%
165
0.1%
151
 
< 0.1%
134
0.1%
123
0.1%

replies_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct589
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.2649955
Minimum0
Maximum9914
Zeros14
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2021-09-27T15:05:14.026600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q140
median90
Q3174
95-th percentile555.5
Maximum9914
Range9914
Interquartile range (IQR)134

Descriptive statistics

Standard deviation448.6132326
Coefficient of variation (CV)2.474902732
Kurtosis181.1043762
Mean181.2649955
Median Absolute Deviation (MAD)60
Skewness11.47444896
Sum607419
Variance201253.8324
MonotonicityNot monotonic
2021-09-27T15:05:14.184293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
533
 
1.0%
3030
 
0.9%
1228
 
0.8%
2027
 
0.8%
4927
 
0.8%
1427
 
0.8%
2126
 
0.8%
5326
 
0.8%
4326
 
0.8%
725
 
0.7%
Other values (579)3076
91.8%
ValueCountFrequency (%)
014
0.4%
122
0.7%
224
0.7%
323
0.7%
417
0.5%
533
1.0%
624
0.7%
725
0.7%
818
0.5%
916
0.5%
ValueCountFrequency (%)
99141
< 0.1%
87631
< 0.1%
78541
< 0.1%
65401
< 0.1%
62171
< 0.1%
62011
< 0.1%
58361
< 0.1%
48631
< 0.1%
45441
< 0.1%
35611
< 0.1%

retweets_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct648
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.853775
Minimum0
Maximum35956
Zeros11
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2021-09-27T15:05:14.339736image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q136
median100
Q3202
95-th percentile639.5
Maximum35956
Range35956
Interquartile range (IQR)166

Descriptive statistics

Standard deviation982.4743157
Coefficient of variation (CV)4.330870473
Kurtosis782.5084178
Mean226.853775
Median Absolute Deviation (MAD)74
Skewness24.98091088
Sum760187
Variance965255.781
MonotonicityNot monotonic
2021-09-27T15:05:14.496539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
336
 
1.1%
936
 
1.1%
2731
 
0.9%
431
 
0.9%
830
 
0.9%
2329
 
0.9%
1929
 
0.9%
529
 
0.9%
728
 
0.8%
1627
 
0.8%
Other values (638)3045
90.9%
ValueCountFrequency (%)
011
 
0.3%
117
0.5%
220
0.6%
336
1.1%
431
0.9%
529
0.9%
627
0.8%
728
0.8%
830
0.9%
936
1.1%
ValueCountFrequency (%)
359561
< 0.1%
290141
< 0.1%
179911
< 0.1%
110291
< 0.1%
77891
< 0.1%
75951
< 0.1%
75371
< 0.1%
69141
< 0.1%
65231
< 0.1%
61451
< 0.1%

likes_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1852
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1204.185616
Minimum0
Maximum46692
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2021-09-27T15:05:14.655123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile62
Q1372.5
median801
Q31393.5
95-th percentile3506
Maximum46692
Range46692
Interquartile range (IQR)1021

Descriptive statistics

Standard deviation1931.216281
Coefficient of variation (CV)1.603752989
Kurtosis144.8121332
Mean1204.185616
Median Absolute Deviation (MAD)493
Skewness9.312195808
Sum4035226
Variance3729596.324
MonotonicityNot monotonic
2021-09-27T15:05:14.813852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39
 
0.3%
1618
 
0.2%
1397
 
0.2%
8017
 
0.2%
1067
 
0.2%
437
 
0.2%
2617
 
0.2%
7737
 
0.2%
277
 
0.2%
2526
 
0.2%
Other values (1842)3279
97.9%
ValueCountFrequency (%)
01
 
< 0.1%
23
 
0.1%
39
0.3%
42
 
0.1%
51
 
< 0.1%
64
0.1%
73
 
0.1%
81
 
< 0.1%
94
0.1%
102
 
0.1%
ValueCountFrequency (%)
466921
< 0.1%
282971
< 0.1%
278881
< 0.1%
256051
< 0.1%
224711
< 0.1%
222161
< 0.1%
215331
< 0.1%
201161
< 0.1%
197401
< 0.1%
163771
< 0.1%

number of tweets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.87347061
Minimum1
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2021-09-27T15:05:14.966918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q315
95-th percentile28
Maximum133
Range132
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.187602801
Coefficient of variation (CV)0.8449558687
Kurtosis11.1544636
Mean10.87347061
Median Absolute Deviation (MAD)6
Skewness1.955514747
Sum36437
Variance84.41204523
MonotonicityNot monotonic
2021-09-27T15:05:15.134444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1286
 
8.5%
2268
 
8.0%
3240
 
7.2%
7182
 
5.4%
5172
 
5.1%
4171
 
5.1%
6167
 
5.0%
8166
 
5.0%
10155
 
4.6%
12148
 
4.4%
Other values (45)1396
41.7%
ValueCountFrequency (%)
1286
8.5%
2268
8.0%
3240
7.2%
4171
5.1%
5172
5.1%
6167
5.0%
7182
5.4%
8166
5.0%
9136
4.1%
10155
4.6%
ValueCountFrequency (%)
1331
 
< 0.1%
661
 
< 0.1%
611
 
< 0.1%
591
 
< 0.1%
561
 
< 0.1%
531
 
< 0.1%
501
 
< 0.1%
492
0.1%
472
0.1%
463
0.1%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct2742
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.4167666
Minimum44.5
Maximum149.4100037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2021-09-27T15:05:15.299018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum44.5
5-th percentile52.85666529
Q161.96000036
median68.41666667
Q382.0749979
95-th percentile133.0133355
Maximum149.4100037
Range104.9100037
Interquartile range (IQR)20.11499755

Descriptive statistics

Standard deviation25.15097148
Coefficient of variation (CV)0.3207346154
Kurtosis0.5569027693
Mean78.4167666
Median Absolute Deviation (MAD)8.796666463
Skewness1.277454835
Sum262774.5849
Variance632.5713662
MonotonicityNot monotonic
2021-09-27T15:05:15.462142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.509998327
 
0.2%
63.270000466
 
0.2%
62.389999396
 
0.2%
59.756
 
0.2%
62.470001226
 
0.2%
57.256
 
0.2%
61.255
 
0.1%
63.889999395
 
0.1%
63.840000155
 
0.1%
75.470001225
 
0.1%
Other values (2732)3294
98.3%
ValueCountFrequency (%)
44.52
0.1%
44.639999391
< 0.1%
45.009998321
< 0.1%
45.030000051
< 0.1%
45.069999691
< 0.1%
45.270000461
< 0.1%
45.299999241
< 0.1%
45.340000151
< 0.1%
45.349998471
< 0.1%
45.446666721
< 0.1%
ValueCountFrequency (%)
149.41000371
< 0.1%
149.33999631
< 0.1%
148.89999391
< 0.1%
148.49000551
< 0.1%
148.33999631
< 0.1%
148.08999631
< 0.1%
148.03999331
< 0.1%
147.89333091
< 0.1%
147.81000261
< 0.1%
147.77000431
< 0.1%

percent change
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct3309
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.000302198429
Minimum-0.06843111568
Maximum0.1084821756
Zeros38
Zeros (%)1.1%
Negative1581
Negative (%)47.2%
Memory size26.3 KiB
2021-09-27T15:05:15.626373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.06843111568
5-th percentile-0.01682789332
Q1-0.004948755432
median0.0003068140092
Q30.005462388263
95-th percentile0.01696509965
Maximum0.1084821756
Range0.1769132913
Interquartile range (IQR)0.01041114369

Descriptive statistics

Standard deviation0.01139192844
Coefficient of variation (CV)37.69684864
Kurtosis8.12829414
Mean0.000302198429
Median Absolute Deviation (MAD)0.005192809984
Skewness0.4855065572
Sum1.012666936
Variance0.0001297760335
MonotonicityNot monotonic
2021-09-27T15:05:15.797756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038
 
1.1%
0.0029550198752
 
0.1%
0.0074785061252
 
0.1%
0.0034488355162
 
0.1%
0.0037865959812
 
0.1%
0.00030529226062
 
0.1%
-0.015334984991
 
< 0.1%
0.0056135811191
 
< 0.1%
0.0066282989671
 
< 0.1%
-0.014038977311
 
< 0.1%
Other values (3299)3299
98.4%
ValueCountFrequency (%)
-0.068431115681
< 0.1%
-0.068124961851
< 0.1%
-0.064130635481
< 0.1%
-0.051243498691
< 0.1%
-0.049502703971
< 0.1%
-0.049305830991
< 0.1%
-0.048370801151
< 0.1%
-0.048313183751
< 0.1%
-0.047862157921
< 0.1%
-0.047483699841
< 0.1%
ValueCountFrequency (%)
0.10848217561
< 0.1%
0.077658056551
< 0.1%
0.076473500371
< 0.1%
0.065377389441
< 0.1%
0.062591955531
< 0.1%
0.059395673631
< 0.1%
0.057874965671
< 0.1%
0.055830809421
< 0.1%
0.054991129491
< 0.1%
0.054676286941
< 0.1%

bins
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
no change
2662 
rise
349 
drop
340 

Length

Max length9
Median length9
Mean length7.971948672
Min length4

Characters and Unicode

Total characters26714
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno change
2nd rowno change
3rd rowno change
4th rowno change
5th rowno change

Common Values

ValueCountFrequency (%)
no change2662
79.4%
rise349
 
10.4%
drop340
 
10.1%

Length

2021-09-27T15:05:16.060896image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:05:16.139591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no2662
44.3%
change2662
44.3%
rise349
 
5.8%
drop340
 
5.7%

Most occurring characters

ValueCountFrequency (%)
n5324
19.9%
e3011
11.3%
o3002
11.2%
2662
10.0%
c2662
10.0%
h2662
10.0%
a2662
10.0%
g2662
10.0%
r689
 
2.6%
i349
 
1.3%
Other values (3)1029
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24052
90.0%
Space Separator2662
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n5324
22.1%
e3011
12.5%
o3002
12.5%
c2662
11.1%
h2662
11.1%
a2662
11.1%
g2662
11.1%
r689
 
2.9%
i349
 
1.5%
s349
 
1.5%
Other values (2)680
 
2.8%
Space Separator
ValueCountFrequency (%)
2662
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin24052
90.0%
Common2662
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n5324
22.1%
e3011
12.5%
o3002
12.5%
c2662
11.1%
h2662
11.1%
a2662
11.1%
g2662
11.1%
r689
 
2.9%
i349
 
1.5%
s349
 
1.5%
Other values (2)680
 
2.8%
Common
ValueCountFrequency (%)
2662
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII26714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n5324
19.9%
e3011
11.3%
o3002
11.2%
2662
10.0%
c2662
10.0%
h2662
10.0%
a2662
10.0%
g2662
10.0%
r689
 
2.6%
i349
 
1.3%
Other values (3)1029
 
3.9%

Interactions

2021-09-27T15:04:50.775694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:50.917984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:51.053153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:51.191921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:51.322639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:51.450675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:51.580082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:51.716582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:51.853252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:51.979786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:52.121628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:52.260043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:52.399446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:52.529963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:52.656676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:52.788927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:52.912049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:53.037219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:53.163147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:53.288638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:53.429763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:53.553744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:53.684205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:53.808864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:53.945717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:54.083198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:54.218119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:54.357167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:54.487239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:54.618362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:54.753024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:54.886819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:55.027248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:55.157503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:55.292740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:55.423349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:55.570716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:55.697547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:55.818921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:55.946157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:56.063051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:56.182327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:56.305407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:56.449607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:56.581272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:56.702730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:56.873795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:57.054216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:57.185486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:57.311890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:57.436507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:57.564082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:57.685775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:57.812030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:57.934069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:58.062313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:58.195512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:58.314284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:58.438315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:58.560069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:58.692459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:58.822740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:58.948620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:59.079367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:59.204431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:59.326571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:59.450989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:59.576165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:59.707996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:59.829782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:59.968323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:00.094779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:00.230462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:00.357886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:00.481709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:00.610948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:00.732687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:00.855209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:00.980103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:01.103283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:01.234589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:01.355052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:01.480691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:01.602533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:01.735620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:01.872339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:02.009809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:02.155838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:02.289264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:02.426685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:02.560180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:02.691963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:02.830705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:02.959937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:03.094364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:03.229668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:03.369088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:03.490512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:03.614449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:03.740788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:03.857101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:03.975570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:04.094566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:04.222710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:04.363025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:04.478081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:04.598678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:04.715892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:04.842698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:04.975015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:05.105540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:05.244192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:05.368148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:05.493738image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:05.644913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:05.772295image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:05.905270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:06.030576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:06.162416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:06.293129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:06.456871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:06.590077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:06.714905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:06.844882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:06.968634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:07.089374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:07.214041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:07.335423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:07.465164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:07.584883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:07.709959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:07.838990image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:07.970366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:08.109168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:08.247183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:08.390837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:08.523673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:08.657226image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:08.803208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:08.939929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:09.081702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:09.214545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:09.352125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:05:09.484922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-27T15:05:16.237814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-27T15:05:16.477071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-27T15:05:16.686833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-27T15:05:16.903385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-27T15:05:17.085827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-27T15:05:09.766690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-27T15:05:10.120484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

datetweetusernamementionshashtagscashtagsvideophotosurlsreplies_countretweets_countlikes_countnumber of tweetspricepercent changebins
02016-08-23 09:30:00@Azphxmex @art_lucero - please look into it ASAP @mgcarley @TMobile You’re wearing the wrong color ;) @tsukinoxo @TMobile do it!!! @mikusz @ATT @TMobile @ATTCares Good choice :)))) @Abruuh_ @TMobile Welcome back! Ready to #GetThanked?! @Mikesmith2317 Come on over!! @Prttyawsmcool Email me! John.legere@t-mobile.com Well, there goes my productivity for the day https://t.co/vxZTn2raCG The future of watching the #Olympics is on mobile, but I think we already knew that! ;) https://t.co/FCsSqRPJMN @isnotgraham @art_lucero john.legere@t-mobile.com @isnotgraham @art_lucero can you email me? Oooo, I need this! @thenorthface, how do I get one in #magenta?!? #gadgetlover https://t.co/xWKZXcSd5X More things to #BingeOn from @Netflix!! https://t.co/Y8IikGMJH9 Hey @MichaelPhelps - I've got your back!! :)))) Watch this: https://t.co/B1XR0MHSjk https://t.co/vnsLUmLuCg Interesting arguments for/against wearables on the field. #MLB https://t.co/IRraM3onkr Use these tips to get tickets, and @tmobile to get free wifi while you’re flying. #tmobilewingman https://t.co/jwffPWCP9FJohnLegere670117521005641646.9599990.000000no change
12016-08-23 16:00:00@silods yes! Very proud of @UMassAmherst and to have studied there! https://t.co/sjK4IbTy7z @iSocialFanz possibly :)) This is hilarious, but also brilliant!! https://t.co/9wtLj2J0qa I might wear one if it were #magenta, @Xbox…. ;) https://t.co/Mk3EI3Q37R #TuesdayMotivation: Today is #TMobileTuesdays &amp; you can #GetThanked w/ awesome prizes from @Orbitz!! #TMobileTuesdays is giving away a chance to win tickets to anywhere in North America!! Where would u go?!? https://t.co/8FGkPmbUYGJohnLegere350113274212929746.830002-0.002768no change
22016-08-24 09:30:00@Woofdah @TMobile What are you going to rent?! @FreelancerJenny 👍🏼 @CURLZ_92 The best day of the week!! @SCAFFBEEZYjr They should switch! @lennahs @TMobile good choice! I think this means I technically have a gold medal ;) https://t.co/CC9KXb07Ch Add this to the list of ways not to impress a date https://t.co/gIGqMvBd1t So great!! You need to hear what we’re cooking up for @TMobileAtWork customers, I think you’d like it! https://t.co/lTCXDP5ero Watching #TheProfit, love the show. @MarcusLemonis does this shirt come in magenta?! https://t.co/S262GTf1OQ @irusanovschi stop thinking and come over!!!! @_Lucky45 i read all email and follow up... No boiler rooms @TMobile :) It’s #TravelTuesday &amp; #TMobileTuesdays!! Coincidence?? Nope!! Now, go try to win cool @Orbitz stuff!! I wonder what would come up if you draw @ATT?! #DeatthStar LOL https://t.co/MvuHgF7QxD Today I dress in what I love! All @TMobile (well I do every day)! ...it's also a great way to meet people :)) https://t.co/iqQAaTLXnm Flying out of Seattle on a beautiful day... Many stops ahead in next week..a lot of @TMobile people to thank! https://t.co/IQ5n8Yz6L6JohnLegere74033412718614531546.660000-0.003630no change
32016-08-24 16:00:00Sunscreen doesn’t smell great, but I don’t think fried chicken-scented sunscreen is any better… https://t.co/VT6qgGquMK Our thoughts &amp; prayers are with 🇮🇹. Fees for @TMobile custs to call/text to/from Italy will be waived until 8/28. https://t.co/wcw2ZY98aL I read every email I get, no matter what :))))) https://t.co/AURnzcFZV5 Is this what the future looks like??? https://t.co/3KU7cnqv5fJohnLegere10000470215712446.209999-0.009644no change
42016-08-25 09:30:00@MillerPhelan Thank you!! @LIVEWlRED You should! @Beverly330 @TMobile welcome!! @petersterne I ❤️ @TMobile ;) @ArimHerrera Switch to @TMobile! :)))) @ItsTonyWhite There’s always next week! @AlienReconstar Set reminders! ;) Great day for a run in @CentralParkNYC of course in all @TMobile #Magenta https://t.co/pK5yZZQZmBJohnLegere4101102715240846.3100010.002164no change
52016-08-25 16:00:00#TBT to that time we surprised the country with #uncarrier12!! Just last week!! https://t.co/k74aVvYxwL @TheresaRockFace @TMobile @ATT Definitely come back to @tmobile! :) Who knew you needed a to-do list hack!? Good tips, though! 📝 https://t.co/9cUGS5OJiy Flying today and still not convinced that you can't run on clouds! Looks like you should be able to doesn't it? :) https://t.co/5qCk33yaWJ But where's the magenta-wearing #CEO emoji??? https://t.co/IuhVb06vbH How do I get one of these for my office?!? #ineedit https://t.co/4RkGxhwIN4 We could send @LegereDoll for a test visit!! ;) https://t.co/1m4WAhgtl0JohnLegere240115103133840746.279999-0.000648no change
62016-08-26 09:30:00@imjus_selfmade @Whiplashjay_ @TMobile deal! @ocmarcelle Good choice :) @Iamsolomon_c #Magenta FTW! @ZacharyKhan3 ;) @Whiplashjay_ Switch to @TMobile!! @MrsNiaB Tuesday’s are the best now!! Does this mean we could see #uber deliveries?! Hmmmm…. https://t.co/3iwj2Bx3gUJohnLegere3200013144163746.4199980.003025no change
72016-08-26 16:00:00LIVE on #Periscope: @TMobile #CEO in #Birmingham! Let’s meet the amazing @TMobile team here!! https://t.co/qRq66z6OnH Happy #NationalDogDay &amp; #RuffFriday! No #LegereDog yet, but go adopt Apple at @SeattleHumane https://t.co/LYRsyxal0f https://t.co/uU3RVGUWrl My #FridayFeeling!! What’s yours? https://t.co/bu25nlaa1b Up up and away... Everyday!!! Good bye Seattle and next stop Birmingham!!!! https://t.co/XzGlEjmOoM Or you could just use a #slowcooker, like me! https://t.co/OQOEBPyhyQ @KingspiritTravl But, #IAmBatman!!!JohnLegere39033381194873645.959999-0.009910no change
82016-08-27 09:30:00@rosyna @evepeyser not blocked any more :) , oops or did I ruin your membership to the who blocked you club? :) sorry either way 🤔 This will be great for future #LegereDog!! https://t.co/qLez4HOT3o .@trackjenny @TMobile Glad @TMobile could help you enjoy #Rio!! @monikarun @TMobile @ATT we have free things for all @MiaStJohnBoxer @Zeola3 @kristoffstjohn1 i would be glad to help any way I can @HenryBoomsma Where ya headed?! @KeishKeish1 You guys are awesome!! @KeishKeish1 @theSamsungSide ;) @ChristinaBhamAl Thanks for having me!! @thutcherson4 @jgebing Nice to hang out with all of you!! @ProfStuckey @TMobile It’s going to be great!! You have until Sunday at 5pm ET &amp; then I’ll pick 5 winners!! Good luck!! Terms &amp; Conditions: https://t.co/qQhmGVJE70 Wow! U all ❤️ @ElectricZooNY! If u want a pair of VIP tix + ferry passes use #TMOEZoo &amp; tag the artist ur most excited to see!! Must be 21+ Did you guys know that @TMobile is sponsoring @ElectricZooNY next weekend?!? I’ll be there!! RT if u wanna come!! I might have extra tix ;) @Zeola3 @MiaStJohnBoxer I agree. Let me know how I can help @Xaighen @art_lucero yes!!!! Today I was in #Birmingham to say #ThankYou to the best team on the 🌎!! I 💗 them!! https://t.co/z0JSTzDuau If they keep updating, I’m going to guess FOREVER… https://t.co/oFZlreencaJohnLegere75014326442417091846.4933320.011604rise
92016-08-27 16:00:00Want a pair of VIP tix + ferry passes to @ElectricZooNY?!? U can enter until tmrw!! Go!!!! https://t.co/qQhmGVJE70 https://t.co/Wz3e4rTDLh After a busy week go the countryside, breathe deep, ok now back to work :))) https://t.co/7Bl1CatKgC Can I get a magenta plaid speaker, @WoolrichInc??? :)))) https://t.co/e7A6dd9SFe Props to our field engineers!! They run into some interesting things... 🐍 https://t.co/Aua8gIrOdD Time to check your #TMobileTuesdays app!! Next week you can save $ on gas w/ @Shell!! You might even win a new car!! ;) @awonderdj Can I help? email me! john.legere@t-mobile.comJohnLegere31011490158830646.216666-0.005951no change

Last rows

datetweetusernamementionshashtagscashtagsvideophotosurlsreplies_countretweets_countlikes_countnumber of tweetspricepercent changebins
33412021-07-15 16:00:00Big congratulations to @Candace_Parker! Can’t wait to buy! https://t.co/QNmxa35kMR See you never, Fleets! https://t.co/6bATAKXEvlJohnLegere100002651062148.339996-0.006696no change
33422021-07-16 09:30:00Congratulations to @neilbarua and the @ServiceMax team!!! https://t.co/AFaIqM1I48 More additions to Clubhouse! Who still actively uses??! https://t.co/OCwsKhscIvJohnLegere20000238452148.089996-0.001685no change
33432021-07-16 16:00:00Trust science! It’s important to continue staying safe, the COVID-19 pandemic isn’t over yet! https://t.co/HBhDZCbvrk BUT WHERE IS THE SOLID PINK HEART?!! https://t.co/wF8LWtG6dxJohnLegere00000236221612149.4100040.008914no change
33442021-07-17 09:30:00Another step forward for accessibility on Twitter. https://t.co/kiC4DpDOL2JohnLegere00000118221147.893331-0.010151no change
33452021-07-17 16:00:00I finally found a way to go to the beach without having to go ON the beach (except to 🏊 in ocean of course) 🌞 🌊 https://t.co/IPXvQZVijB But how will I know when I’m going to get scammed?!! https://t.co/L0slvP3HPG My kind of charcuterie board! https://t.co/6BhW2V747Z Happy #WorldEmojiDay! Which is your favorite?? https://t.co/Px8HHWYOvEJohnLegere01012345243404147.810003-0.000563no change
33462021-07-18 16:00:00Who’s subscribing?? https://t.co/bZr4a84P2X Hey New Yorkers – need an officiant?! https://t.co/PbBecmjgu0 is it possible that the ocean makes the morning coffee ☕️ even better than it's normal great? 😊 https://t.co/43jhIroufdJohnLegere0001121412712183146.210002-0.010066no change
33472021-07-19 09:30:00Number 16 is a movie night game changer!! https://t.co/9YJS0H383PJohnLegere00000152321147.5000000.008823no change
33482021-07-19 16:00:00Gorgeous! Maybe I should charter a magenta rocket. https://t.co/jx2ZL0gX6Q So I'm about to go for a run on the beach.....am i the only one that doesn't think these ☁️ mean 🌧 ☔️ is coming? https://t.co/fMxzuOZ0nF Always love a good Quesarito https://t.co/KaiMmXEc5q What I’ve learned from @Lifehacker is that I do EVERYTHING wrong! https://t.co/xAbzXxZhqZJohnLegere10012325172364144.610001-0.019593drop
33492021-07-20 09:30:00Call on John is POSTPONED for today but tune in on Thursday, July 22nd at 12 PM ET on Instagram Stories for an extra special Call on John! What do you want to see me give away?? https://t.co/4P3hucQ3ioJohnLegere00011073181144.7899930.001245no change
33502021-07-20 16:00:00WOW this looks so delicious! https://t.co/hnOKodchlg Up up and away! https://t.co/jvRcpkqng3JohnLegere0000022891152144.399994-0.002694no change